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精神健康研究中的种族和种族化问题及其对开发和评估机器学习模型的影响：快速综述。

Race and Racialization in Mental Health Research and Implications for Developing and Evaluating Machine Learning Models: A Rapid Review.

机构信息

Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Canada.

Campbell Family Mental Health Research Institute, CAMH, Toronto, Canada.

出版信息

Stud Health Technol Inform. 2022 Jun 6;290:1088-1089. doi: 10.3233/SHTI220281.

DOI:10.3233/SHTI220281
PMID:35673219
Abstract

Machine learning models are often trained on sociodemographic features to predict mental health outcomes. Biases in the collection of race-related data can limit the development of useful and fair models. To assess the current state of this data in mental health research, we conducted a rapid review guided by Critical Race Theory. Findings reveal limitations in the measurement and reporting of race and ethnicity, potentially leading to models that amplify health inequities.

摘要

机器学习模型通常使用社会人口统计学特征进行训练，以预测心理健康结果。与种族相关的数据收集过程中的偏差可能会限制有用和公平模型的开发。为了评估心理健康研究中此类数据的现状，我们在批判种族理论的指导下进行了快速审查。研究结果表明，在种族和民族的测量和报告方面存在局限性，这可能导致模型加剧健康不平等。

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